Project Summary/Abstract Suicide is among the 10 leading causes of death in the U.S. and suicide risk increases substantially after age 65. Nearly 70% of individuals currently 65 years old will require long-term care during the remainder of their lives. The nation's 15,000 nursing homes are an important setting providing residential post-acute and long-term care to over 3 million older and disabled Americans every year, and the nation's 30,000 assisted living facilities provided a home to about 800,000 elders during 2013-14. Residents in the 2 types of long-term care facilities are often socially isolated, physically and cognitively disabled, and diagnosed with multiple mental and medical conditions, all of which are associated with suicide in older adults. As a result, they may show elevated risk taking their own lives compared to community-living elders despite the fact that long-term care facilities are able to monitor closely the daily activities of their residents. However, the actual suicide risk among these residents is largely unknown. Moreover, it is expected that organizational characteristics and overall ability of long-term care facilities to provide safe and high quality of care play an important role in shaping the risk of suicide for residents at different points of care. However, empirical evidence on these questions is extremely limited. This study proposes to evaluate systematically the risk for suicide mortality among nursing home and assisted living residents, estimate suicide risk for nursing home residents at different points of care using longitudinal resident assessment records, and determine the associations of organizational characteristics and state policies with risk-adjusted suicide death rate. Organizational characteristics will be measured by nursing home structural factors (e.g. profit status), overall quality of care (e.g. measured by nurse staffing level), and organizational-level patient safety culture. This project will link the longitudinal National Violent Death Reporting System (NVDRS) and National Death Index (NDI) files to nursing home Minimum Data Sets (MDS) and Medicare beneficiary data, as well as other facility and state policy files. These data will be used to model resident risk for suicide death using cutting-edge statistical machine learning methods, quantify the variations in risk-adjusted suicide death rates at different levels (i.e. facility, region, and state levels), and test the hypotheses regarding organizational and policy impacts on suicide death. The information generated in this project will contribute to the knowledge of suicide burdens among the most vulnerable elders in need of residential long-term care. The knowledge gained will inform future organizational- and policy-level interventions to prevent suicide among elders receiving long-term care.